343 citations · 1.3k across the 50 of their papers we have counts for
8 papers · 1 filter
Orthros: Non-autoregressive End-to-end Speech Translation with Dual-decoder
Hirofumi Inaguma, Yosuke Higuchi, Kevin Duh +2
Fast inference speed is an important goal towards real-world deployment of speech translation (ST) systems. End-to-end (E2E) models based on the encoder-decoder architecture are mo…
Very Deep Transformers for Neural Machine Translation
Xiaodong Liu, Kevin Duh, Liyuan Liu +1
We explore the application of very deep Transformer models for Neural Machine Translation (NMT). Using a simple yet effective initialization technique that stabilizes training, we…
Modeling Document Interactions for Learning to Rank with Regularized Self-Attention
Shuo Sun, Kevin Duh
Learning to rank is an important task that has been successfully deployed in many real-world information retrieval systems. Most existing methods compute relevance judgments of doc…
ESPnet-ST: All-in-One Speech Translation Toolkit
Hirofumi Inaguma, Shun Kiyono, Kevin Duh +4
We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech…
When Does Unsupervised Machine Translation Work?
Kelly Marchisio, Kevin Duh, Philipp Koehn
Despite the reported success of unsupervised machine translation (MT), the field has yet to examine the conditions under which these methods succeed, and where they fail. We conduc…
Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation
Mitchell A. Gordon, Kevin Duh
We explore best practices for training small, memory efficient machine translation models with sequence-level knowledge distillation in the domain adaptation setting. While both do…
Machine Translation System Selection from Bandit Feedback
Jason Naradowsky, Xuan Zhang, Kevin Duh
Adapting machine translation systems in the real world is a difficult problem. In contrast to offline training, users cannot provide the type of fine-grained feedback (such as corr…
Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning
Mitchell A. Gordon, Kevin Duh, Nicholas Andrews
Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models…